Overview

Dataset statistics

Number of variables28
Number of observations7406
Missing cells7405
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory232.0 B

Variable types

Categorical11
DateTime1
TimeSeries12
Numeric4

Timeseries statistics

Number of series12
Time series length7406
Starting point2010-10-01 00:00:00
Ending point2016-03-31 00:00:00
Period6 hours, 30 minutes and 29.85 seconds
2023-10-10T11:19:23.345135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:24.554178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Alerts

State Code has constant value ""Constant
County Code has constant value ""Constant
Site Num has constant value ""Constant
Address has constant value ""Constant
State has constant value ""Constant
County has constant value ""Constant
City has constant value ""Constant
NO2 Units has constant value ""Constant
O3 Units has constant value ""Constant
SO2 Units has constant value ""Constant
CO Units has constant value ""Constant
NO2 Mean is highly overall correlated with NO2 1st Max Value and 6 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
NO2 AQI is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
O3 Mean is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 Mean and 3 other fieldsHigh correlation
O3 AQI is highly overall correlated with NO2 Mean and 3 other fieldsHigh correlation
SO2 Mean is highly overall correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with SO2 Mean and 1 other fieldsHigh correlation
SO2 AQI is highly overall correlated with SO2 Mean and 1 other fieldsHigh correlation
CO Mean is highly overall correlated with NO2 Mean and 6 other fieldsHigh correlation
CO 1st Max Value is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
CO AQI is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
SO2 AQI has 3702 (50.0%) missing valuesMissing
CO AQI has 3703 (50.0%) missing valuesMissing
NO2 Mean is non stationaryNon stationary
NO2 1st Max Value is non stationaryNon stationary
NO2 1st Max Hour is non stationaryNon stationary
NO2 AQI is non stationaryNon stationary
O3 Mean is non stationaryNon stationary
O3 1st Max Value is non stationaryNon stationary
O3 AQI is non stationaryNon stationary
SO2 Mean is non stationaryNon stationary
SO2 1st Max Value is non stationaryNon stationary
CO Mean is non stationaryNon stationary
CO AQI is non stationaryNon stationary
NO2 Mean is seasonalSeasonal
NO2 1st Max Value is seasonalSeasonal
NO2 1st Max Hour is seasonalSeasonal
NO2 AQI is seasonalSeasonal
O3 Mean is seasonalSeasonal
O3 1st Max Value is seasonalSeasonal
O3 AQI is seasonalSeasonal
SO2 Mean is seasonalSeasonal
SO2 1st Max Value is seasonalSeasonal
CO Mean is seasonalSeasonal
CO AQI is seasonalSeasonal
NO2 1st Max Hour has 612 (8.3%) zerosZeros
SO2 Mean has 290 (3.9%) zerosZeros
SO2 1st Max Value has 298 (4.0%) zerosZeros
SO2 1st Max Hour has 288 (3.9%) zerosZeros
SO2 AQI has 2596 (35.1%) zerosZeros
CO 1st Max Hour has 2491 (33.6%) zerosZeros

Reproduction

Analysis started2023-10-10 11:18:11.155119
Analysis finished2023-10-10 11:19:22.500109
Duration1 minute and 11.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
4
7406 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7406
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 7406
100.0%

Length

2023-10-10T11:19:25.170185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:25.439664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 7406
100.0%

Most occurring characters

ValueCountFrequency (%)
4 7406
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7406
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7406
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7406
100.0%

County Code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
19
7406 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14812
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
19 7406
100.0%

Length

2023-10-10T11:19:25.687525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:25.911110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
19 7406
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7406
50.0%
9 7406
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14812
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7406
50.0%
9 7406
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14812
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7406
50.0%
9 7406
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7406
50.0%
9 7406
50.0%

Site Num
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
1028
7406 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29624
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1028
2nd row1028
3rd row1028
4th row1028
5th row1028

Common Values

ValueCountFrequency (%)
1028 7406
100.0%

Length

2023-10-10T11:19:26.149966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:26.377165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1028 7406
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7406
25.0%
0 7406
25.0%
2 7406
25.0%
8 7406
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7406
25.0%
0 7406
25.0%
2 7406
25.0%
8 7406
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7406
25.0%
0 7406
25.0%
2 7406
25.0%
8 7406
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7406
25.0%
0 7406
25.0%
2 7406
25.0%
8 7406
25.0%

Address
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
400 W RIVER ROAD
7406 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters118496
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row400 W RIVER ROAD
2nd row400 W RIVER ROAD
3rd row400 W RIVER ROAD
4th row400 W RIVER ROAD
5th row400 W RIVER ROAD

Common Values

ValueCountFrequency (%)
400 W RIVER ROAD 7406
100.0%

Length

2023-10-10T11:19:26.607013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:26.835403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
400 7406
25.0%
w 7406
25.0%
river 7406
25.0%
road 7406
25.0%

Most occurring characters

ValueCountFrequency (%)
22218
18.8%
R 22218
18.8%
0 14812
12.5%
4 7406
 
6.2%
W 7406
 
6.2%
I 7406
 
6.2%
V 7406
 
6.2%
E 7406
 
6.2%
O 7406
 
6.2%
A 7406
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 74060
62.5%
Space Separator 22218
 
18.8%
Decimal Number 22218
 
18.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 22218
30.0%
W 7406
 
10.0%
I 7406
 
10.0%
V 7406
 
10.0%
E 7406
 
10.0%
O 7406
 
10.0%
A 7406
 
10.0%
D 7406
 
10.0%
Decimal Number
ValueCountFrequency (%)
0 14812
66.7%
4 7406
33.3%
Space Separator
ValueCountFrequency (%)
22218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74060
62.5%
Common 44436
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 22218
30.0%
W 7406
 
10.0%
I 7406
 
10.0%
V 7406
 
10.0%
E 7406
 
10.0%
O 7406
 
10.0%
A 7406
 
10.0%
D 7406
 
10.0%
Common
ValueCountFrequency (%)
22218
50.0%
0 14812
33.3%
4 7406
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22218
18.8%
R 22218
18.8%
0 14812
12.5%
4 7406
 
6.2%
W 7406
 
6.2%
I 7406
 
6.2%
V 7406
 
6.2%
E 7406
 
6.2%
O 7406
 
6.2%
A 7406
 
6.2%

State
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Arizona
7406 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters51842
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona 7406
100.0%

Length

2023-10-10T11:19:27.068373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:27.297531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona 7406
100.0%

Most occurring characters

ValueCountFrequency (%)
A 7406
14.3%
r 7406
14.3%
i 7406
14.3%
z 7406
14.3%
o 7406
14.3%
n 7406
14.3%
a 7406
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44436
85.7%
Uppercase Letter 7406
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7406
16.7%
i 7406
16.7%
z 7406
16.7%
o 7406
16.7%
n 7406
16.7%
a 7406
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51842
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7406
14.3%
r 7406
14.3%
i 7406
14.3%
z 7406
14.3%
o 7406
14.3%
n 7406
14.3%
a 7406
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7406
14.3%
r 7406
14.3%
i 7406
14.3%
z 7406
14.3%
o 7406
14.3%
n 7406
14.3%
a 7406
14.3%

County
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Pima
7406 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29624
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPima
2nd rowPima
3rd rowPima
4th rowPima
5th rowPima

Common Values

ValueCountFrequency (%)
Pima 7406
100.0%

Length

2023-10-10T11:19:27.550729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:27.775329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
pima 7406
100.0%

Most occurring characters

ValueCountFrequency (%)
P 7406
25.0%
i 7406
25.0%
m 7406
25.0%
a 7406
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22218
75.0%
Uppercase Letter 7406
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7406
33.3%
m 7406
33.3%
a 7406
33.3%
Uppercase Letter
ValueCountFrequency (%)
P 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29624
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 7406
25.0%
i 7406
25.0%
m 7406
25.0%
a 7406
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 7406
25.0%
i 7406
25.0%
m 7406
25.0%
a 7406
25.0%

City
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Tucson
7406 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters44436
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTucson
2nd rowTucson
3rd rowTucson
4th rowTucson
5th rowTucson

Common Values

ValueCountFrequency (%)
Tucson 7406
100.0%

Length

2023-10-10T11:19:28.005759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:28.234544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
tucson 7406
100.0%

Most occurring characters

ValueCountFrequency (%)
T 7406
16.7%
u 7406
16.7%
c 7406
16.7%
s 7406
16.7%
o 7406
16.7%
n 7406
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37030
83.3%
Uppercase Letter 7406
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 7406
20.0%
c 7406
20.0%
s 7406
20.0%
o 7406
20.0%
n 7406
20.0%
Uppercase Letter
ValueCountFrequency (%)
T 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 7406
16.7%
u 7406
16.7%
c 7406
16.7%
s 7406
16.7%
o 7406
16.7%
n 7406
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 7406
16.7%
u 7406
16.7%
c 7406
16.7%
s 7406
16.7%
o 7406
16.7%
n 7406
16.7%
Distinct1852
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Minimum2010-10-01 00:00:00
Maximum2016-03-31 00:00:00
2023-10-10T11:19:28.485743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:28.822177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Parts per billion
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 7406
100.0%

Length

2023-10-10T11:19:29.150692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:29.379849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 7406
33.3%
per 7406
33.3%
billion 7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103684
82.4%
Space Separator 14812
 
11.8%
Uppercase Letter 7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 14812
14.3%
i 14812
14.3%
l 14812
14.3%
a 7406
7.1%
t 7406
7.1%
s 7406
7.1%
p 7406
7.1%
e 7406
7.1%
b 7406
7.1%
o 7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111090
88.2%
Common 14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 14812
13.3%
i 14812
13.3%
l 14812
13.3%
P 7406
6.7%
a 7406
6.7%
t 7406
6.7%
s 7406
6.7%
p 7406
6.7%
e 7406
6.7%
b 7406
6.7%
Other values (2) 14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1610
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.371199
Minimum0.834783
Maximum29.409524
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:29.730610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.834783
5-th percentile3.883333
Q16.9010418
median10.175
Q315.313315
95-th percentile21.8375
Maximum29.409524
Range28.574741
Interquartile range (IQR)8.4122735

Descriptive statistics

Standard deviation5.6165304
Coefficient of variation (CV)0.49392597
Kurtosis-0.46198764
Mean11.371199
Median Absolute Deviation (MAD)3.820833
Skewness0.5972069
Sum84215.098
Variance31.545413
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001170396581
2023-10-10T11:19:30.135088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:31.289117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:31.568147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
10.25 16
 
0.2%
5.283333 16
 
0.2%
7.345833 16
 
0.2%
6.783333 16
 
0.2%
5.670833 12
 
0.2%
11.204167 12
 
0.2%
8.833333 12
 
0.2%
9.129167 12
 
0.2%
13.275 12
 
0.2%
16.629167 12
 
0.2%
Other values (1600) 7270
98.2%
ValueCountFrequency (%)
0.834783 4
0.1%
1.4625 4
0.1%
1.766667 4
0.1%
1.770833 4
0.1%
1.7875 4
0.1%
1.8125 4
0.1%
1.916667 4
0.1%
1.958333 4
0.1%
2.004167 4
0.1%
2.008333 4
0.1%
ValueCountFrequency (%)
29.409524 4
0.1%
28.85 4
0.1%
28.2125 4
0.1%
27.8875 4
0.1%
27.325 4
0.1%
26.9125 4
0.1%
26.754167 4
0.1%
26.508333 4
0.1%
26.266667 4
0.1%
26.05 4
0.1%
2023-10-10T11:19:30.457277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct378
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.410424
Minimum3.7
Maximum46.4
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:32.042532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile9.6
Q117.5
median24.5
Q331.2
95-th percentile38.6
Maximum46.4
Range42.7
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation8.9717642
Coefficient of variation (CV)0.36753824
Kurtosis-0.73618433
Mean24.410424
Median Absolute Deviation (MAD)6.8
Skewness-0.033985245
Sum180783.6
Variance80.492553
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.793613435 × 10-6
2023-10-10T11:19:32.437389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:33.360841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:33.642813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
28 56
 
0.8%
17.5 56
 
0.8%
28.6 52
 
0.7%
29.3 52
 
0.7%
33 48
 
0.6%
31 48
 
0.6%
23.2 48
 
0.6%
21.3 48
 
0.6%
15.5 48
 
0.6%
33.7 44
 
0.6%
Other values (368) 6906
93.2%
ValueCountFrequency (%)
3.7 4
0.1%
3.8 4
0.1%
4.2 8
0.1%
4.3 4
0.1%
4.5 4
0.1%
4.6 8
0.1%
4.8 4
0.1%
4.9 4
0.1%
5 4
0.1%
5.2 4
0.1%
ValueCountFrequency (%)
46.4 4
0.1%
46.2 4
0.1%
46.1 4
0.1%
45.8 4
0.1%
45.7 4
0.1%
45.6 4
0.1%
45.5 4
0.1%
45.3 8
0.1%
45.1 4
0.1%
45 8
0.1%
2023-10-10T11:19:32.739135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

NON STATIONARY  SEASONAL  ZEROS 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.455036
Minimum0
Maximum23
Zeros612
Zeros (%)8.3%
Memory size115.7 KiB
2023-10-10T11:19:34.114038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.0553498
Coefficient of variation (CV)0.59868658
Kurtosis-1.5410951
Mean13.455036
Median Absolute Deviation (MAD)5
Skewness-0.2883018
Sum99648
Variance64.88866
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.555712396 × 10-14
2023-10-10T11:19:34.464367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
2023-10-10T11:19:35.418002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:35.736550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
21 956
12.9%
7 764
10.3%
22 748
10.1%
20 716
9.7%
6 698
9.4%
0 612
8.3%
19 564
7.6%
23 540
7.3%
8 464
6.3%
18 340
 
4.6%
Other values (14) 1004
13.6%
ValueCountFrequency (%)
0 612
8.3%
1 168
 
2.3%
2 84
 
1.1%
3 60
 
0.8%
4 40
 
0.5%
5 264
 
3.6%
6 698
9.4%
7 764
10.3%
8 464
6.3%
9 200
 
2.7%
ValueCountFrequency (%)
23 540
7.3%
22 748
10.1%
21 956
12.9%
20 716
9.7%
19 564
7.6%
18 340
 
4.6%
17 72
 
1.0%
16 4
 
0.1%
15 4
 
0.1%
14 4
 
0.1%
2023-10-10T11:19:34.771456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct41
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.57764
Minimum3
Maximum43
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:36.465525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q116
median23
Q329
95-th percentile36
Maximum43
Range40
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.5026973
Coefficient of variation (CV)0.37659815
Kurtosis-0.70688798
Mean22.57764
Median Absolute Deviation (MAD)6
Skewness-0.040483741
Sum167210
Variance72.295862
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.849453197 × 10-6
2023-10-10T11:19:36.881192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
2023-10-10T11:19:37.896461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:38.197059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
25 600
 
8.1%
26 320
 
4.3%
22 316
 
4.3%
21 292
 
3.9%
27 284
 
3.8%
29 284
 
3.8%
20 276
 
3.7%
17 272
 
3.7%
31 272
 
3.7%
18 264
 
3.6%
Other values (31) 4226
57.1%
ValueCountFrequency (%)
3 8
 
0.1%
4 32
 
0.4%
5 36
 
0.5%
6 40
 
0.5%
7 80
 
1.1%
8 216
2.9%
9 120
1.6%
10 196
2.6%
11 172
2.3%
12 196
2.6%
ValueCountFrequency (%)
43 12
 
0.2%
42 52
 
0.7%
41 24
 
0.3%
40 32
 
0.4%
39 72
1.0%
38 44
 
0.6%
37 100
1.4%
36 92
1.2%
35 156
2.1%
34 172
2.3%
2023-10-10T11:19:37.262726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Parts per million
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 7406
100.0%

Length

2023-10-10T11:19:38.592736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:38.819287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 7406
33.3%
per 7406
33.3%
million 7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103684
82.4%
Space Separator 14812
 
11.8%
Uppercase Letter 7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 14812
14.3%
i 14812
14.3%
l 14812
14.3%
a 7406
7.1%
t 7406
7.1%
s 7406
7.1%
p 7406
7.1%
e 7406
7.1%
m 7406
7.1%
o 7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111090
88.2%
Common 14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 14812
13.3%
i 14812
13.3%
l 14812
13.3%
P 7406
6.7%
a 7406
6.7%
t 7406
6.7%
s 7406
6.7%
p 7406
6.7%
e 7406
6.7%
m 7406
6.7%
Other values (2) 14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct841
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.027341289
Minimum0.003125
Maximum0.059167
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:39.184008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.003125
5-th percentile0.011
Q10.018667
median0.027375
Q30.03525
95-th percentile0.0445
Maximum0.059167
Range0.056042
Interquartile range (IQR)0.016583

Descriptive statistics

Standard deviation0.010504456
Coefficient of variation (CV)0.38419752
Kurtosis-0.73608374
Mean0.027341289
Median Absolute Deviation (MAD)0.008167
Skewness0.12706227
Sum202.48959
Variance0.00011034359
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.003658420953
2023-10-10T11:19:39.600268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:40.538977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:40.821374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.020792 32
 
0.4%
0.030917 32
 
0.4%
0.028958 32
 
0.4%
0.030583 28
 
0.4%
0.035583 24
 
0.3%
0.018667 24
 
0.3%
0.025083 24
 
0.3%
0.01375 24
 
0.3%
0.028542 24
 
0.3%
0.037625 24
 
0.3%
Other values (831) 7138
96.4%
ValueCountFrequency (%)
0.003125 4
0.1%
0.004042 4
0.1%
0.004167 4
0.1%
0.0055 4
0.1%
0.006 4
0.1%
0.006042 4
0.1%
0.006083 8
0.1%
0.006125 4
0.1%
0.006375 4
0.1%
0.007417 4
0.1%
ValueCountFrequency (%)
0.059167 4
0.1%
0.058917 4
0.1%
0.056875 4
0.1%
0.056 4
0.1%
0.05425 4
0.1%
0.053333 4
0.1%
0.053167 4
0.1%
0.052958 4
0.1%
0.052333 4
0.1%
0.052125 4
0.1%
2023-10-10T11:19:39.917448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.042756684
Minimum0.008
Maximum0.077
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:41.299243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.026
Q10.035
median0.043
Q30.05
95-th percentile0.061
Maximum0.077
Range0.069
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.010454283
Coefficient of variation (CV)0.24450641
Kurtosis-0.26531984
Mean0.042756684
Median Absolute Deviation (MAD)0.007
Skewness0.066426587
Sum316.656
Variance0.00010929204
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0003248964579
2023-10-10T11:19:41.719590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:42.874757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:43.153843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.043 328
 
4.4%
0.038 322
 
4.3%
0.046 292
 
3.9%
0.041 272
 
3.7%
0.034 272
 
3.7%
0.047 268
 
3.6%
0.048 256
 
3.5%
0.049 256
 
3.5%
0.035 248
 
3.3%
0.044 240
 
3.2%
Other values (55) 4652
62.8%
ValueCountFrequency (%)
0.008 4
 
0.1%
0.01 4
 
0.1%
0.012 8
 
0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 8
 
0.1%
0.017 8
 
0.1%
0.018 8
 
0.1%
0.019 8
 
0.1%
0.02 20
0.3%
ValueCountFrequency (%)
0.077 4
 
0.1%
0.075 4
 
0.1%
0.074 4
 
0.1%
0.073 4
 
0.1%
0.072 4
 
0.1%
0.071 4
 
0.1%
0.069 8
 
0.1%
0.068 12
 
0.2%
0.067 8
 
0.1%
0.066 40
0.5%
2023-10-10T11:19:42.053477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.21037
Minimum0
Maximum23
Zeros68
Zeros (%)0.9%
Memory size115.7 KiB
2023-10-10T11:19:43.635022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8972329
Coefficient of variation (CV)0.18581432
Kurtosis17.492588
Mean10.21037
Median Absolute Deviation (MAD)1
Skewness0.53460477
Sum75618
Variance3.5994927
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.583869238 × 10-23
2023-10-10T11:19:43.973315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
2023-10-10T11:19:44.881660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:45.150432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
10 3080
41.6%
11 2010
27.1%
9 1396
18.8%
12 296
 
4.0%
8 188
 
2.5%
14 76
 
1.0%
13 72
 
1.0%
0 68
 
0.9%
7 40
 
0.5%
15 28
 
0.4%
Other values (11) 152
 
2.1%
ValueCountFrequency (%)
0 68
 
0.9%
2 12
 
0.2%
4 8
 
0.1%
6 16
 
0.2%
7 40
 
0.5%
8 188
 
2.5%
9 1396
18.8%
10 3080
41.6%
11 2010
27.1%
12 296
 
4.0%
ValueCountFrequency (%)
23 16
 
0.2%
22 8
 
0.1%
21 12
 
0.2%
20 16
 
0.2%
19 20
 
0.3%
18 12
 
0.2%
17 12
 
0.2%
16 20
 
0.3%
15 28
 
0.4%
14 76
1.0%
2023-10-10T11:19:44.259520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct58
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.358898
Minimum7
Maximum122
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:45.647717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile23
Q131
median37
Q344
95-th percentile58
Maximum122
Range115
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.386193
Coefficient of variation (CV)0.29683315
Kurtosis4.1133221
Mean38.358898
Median Absolute Deviation (MAD)6
Skewness1.2693955
Sum284086
Variance129.64538
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.635888185 × 10-5
2023-10-10T11:19:46.047790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:47.026109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:47.483733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
31 488
 
6.6%
36 428
 
5.8%
42 332
 
4.5%
32 300
 
4.1%
44 292
 
3.9%
40 292
 
3.9%
39 280
 
3.8%
35 274
 
3.7%
37 260
 
3.5%
47 252
 
3.4%
Other values (48) 4208
56.8%
ValueCountFrequency (%)
7 4
 
0.1%
9 4
 
0.1%
10 4
 
0.1%
11 4
 
0.1%
13 8
0.1%
14 12
0.2%
15 8
0.1%
16 8
0.1%
17 16
0.2%
18 12
0.2%
ValueCountFrequency (%)
122 4
 
0.1%
100 4
 
0.1%
97 4
 
0.1%
93 4
 
0.1%
90 8
 
0.1%
87 8
 
0.1%
80 24
 
0.3%
77 24
 
0.3%
74 28
 
0.4%
71 72
1.0%
2023-10-10T11:19:46.399061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Parts per billion
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 7406
100.0%

Length

2023-10-10T11:19:47.884899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:48.123386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 7406
33.3%
per 7406
33.3%
billion 7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103684
82.4%
Space Separator 14812
 
11.8%
Uppercase Letter 7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 14812
14.3%
i 14812
14.3%
l 14812
14.3%
a 7406
7.1%
t 7406
7.1%
s 7406
7.1%
p 7406
7.1%
e 7406
7.1%
b 7406
7.1%
o 7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111090
88.2%
Common 14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 14812
13.3%
i 14812
13.3%
l 14812
13.3%
P 7406
6.7%
a 7406
6.7%
t 7406
6.7%
s 7406
6.7%
p 7406
6.7%
e 7406
6.7%
b 7406
6.7%
Other values (2) 14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct417
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20693121
Minimum-0.033333
Maximum1.35
Zeros290
Zeros (%)3.9%
Memory size115.7 KiB
2023-10-10T11:19:48.492092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-0.033333
5-th percentile0.008333
Q10.0875
median0.175
Q30.283333
95-th percentile0.525
Maximum1.35
Range1.383333
Interquartile range (IQR)0.195833

Descriptive statistics

Standard deviation0.16928613
Coefficient of variation (CV)0.81807928
Kurtosis3.9406797
Mean0.20693121
Median Absolute Deviation (MAD)0.1
Skewness1.5681313
Sum1532.5325
Variance0.028657795
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.343543333 × 10-14
2023-10-10T11:19:48.921842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:49.948603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:50.229666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 290
 
3.9%
0.1125 184
 
2.5%
0.1 180
 
2.4%
0.15 164
 
2.2%
0.0875 160
 
2.2%
0.075 158
 
2.1%
0.2 152
 
2.1%
0.025 148
 
2.0%
0.1875 142
 
1.9%
0.2125 138
 
1.9%
Other values (407) 5690
76.8%
ValueCountFrequency (%)
-0.033333 2
 
< 0.1%
-0.029167 2
 
< 0.1%
-0.025 2
 
< 0.1%
-0.020833 2
 
< 0.1%
-0.0125 6
 
0.1%
-0.008333 2
 
< 0.1%
0 290
3.9%
0.004167 28
 
0.4%
0.004348 4
 
0.1%
0.004545 6
 
0.1%
ValueCountFrequency (%)
1.35 2
< 0.1%
1.3375 2
< 0.1%
1.121739 2
< 0.1%
1.116667 2
< 0.1%
1.0875 4
0.1%
1.071429 2
< 0.1%
1.0625 2
< 0.1%
1.054167 2
< 0.1%
1.025 2
< 0.1%
0.979167 2
< 0.1%
2023-10-10T11:19:49.283900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct69
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75147178
Minimum0
Maximum12.4
Zeros298
Zeros (%)4.0%
Memory size115.7 KiB
2023-10-10T11:19:50.694440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.5
Q30.9
95-th percentile2.4
Maximum12.4
Range12.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.912497
Coefficient of variation (CV)1.2142798
Kurtosis20.794442
Mean0.75147178
Median Absolute Deviation (MAD)0.3
Skewness3.5773301
Sum5565.4
Variance0.83265077
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.378720586 × 10-16
2023-10-10T11:19:51.106123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:52.131348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:52.444640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.2 910
12.3%
0.3 876
11.8%
0.4 780
10.5%
0.1 776
10.5%
0.5 608
 
8.2%
0.6 470
 
6.3%
0.7 378
 
5.1%
0.9 314
 
4.2%
0 298
 
4.0%
0.8 286
 
3.9%
Other values (59) 1710
23.1%
ValueCountFrequency (%)
0 298
 
4.0%
0.1 776
10.5%
0.2 910
12.3%
0.3 876
11.8%
0.4 780
10.5%
0.5 608
8.2%
0.6 470
6.3%
0.7 378
5.1%
0.8 286
 
3.9%
0.9 314
 
4.2%
ValueCountFrequency (%)
12.4 2
< 0.1%
9.6 2
< 0.1%
7.8 2
< 0.1%
7.7 2
< 0.1%
7.5 2
< 0.1%
7.4 2
< 0.1%
7.3 2
< 0.1%
7.2 2
< 0.1%
6.8 2
< 0.1%
6.6 4
0.1%
2023-10-10T11:19:51.458368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.996219
Minimum0
Maximum23
Zeros288
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:52.804786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median11
Q314
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1134436
Coefficient of variation (CV)0.55595868
Kurtosis-0.61098201
Mean10.996219
Median Absolute Deviation (MAD)3
Skewness0.26072103
Sum81438
Variance37.374192
MonotonicityNot monotonic
2023-10-10T11:19:53.290905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 1396
18.8%
8 1294
17.5%
2 642
8.7%
20 632
8.5%
9 478
 
6.5%
10 362
 
4.9%
23 320
 
4.3%
14 318
 
4.3%
0 288
 
3.9%
7 234
 
3.2%
Other values (14) 1442
19.5%
ValueCountFrequency (%)
0 288
 
3.9%
1 72
 
1.0%
2 642
8.7%
3 44
 
0.6%
4 24
 
0.3%
5 126
 
1.7%
6 80
 
1.1%
7 234
 
3.2%
8 1294
17.5%
9 478
 
6.5%
ValueCountFrequency (%)
23 320
4.3%
22 72
 
1.0%
21 160
 
2.2%
20 632
8.5%
19 190
 
2.6%
18 148
 
2.0%
17 224
 
3.0%
16 38
 
0.5%
15 52
 
0.7%
14 318
4.3%

SO2 AQI
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)0.3%
Missing3702
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.68520518
Minimum0
Maximum17
Zeros2596
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:53.539187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5369207
Coefficient of variation (CV)2.2430079
Kurtosis18.08592
Mean0.68520518
Median Absolute Deviation (MAD)0
Skewness3.6142928
Sum2538
Variance2.3621251
MonotonicityNot monotonic
2023-10-10T11:19:53.804643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 2596
35.1%
1 666
 
9.0%
3 250
 
3.4%
4 88
 
1.2%
6 50
 
0.7%
7 26
 
0.4%
10 12
 
0.2%
9 12
 
0.2%
17 2
 
< 0.1%
13 2
 
< 0.1%
(Missing) 3702
50.0%
ValueCountFrequency (%)
0 2596
35.1%
1 666
 
9.0%
3 250
 
3.4%
4 88
 
1.2%
6 50
 
0.7%
7 26
 
0.4%
9 12
 
0.2%
10 12
 
0.2%
13 2
 
< 0.1%
17 2
 
< 0.1%
ValueCountFrequency (%)
17 2
 
< 0.1%
13 2
 
< 0.1%
10 12
 
0.2%
9 12
 
0.2%
7 26
 
0.4%
6 50
 
0.7%
4 88
 
1.2%
3 250
 
3.4%
1 666
 
9.0%
0 2596
35.1%

CO Units
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Parts per million
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 7406
100.0%

Length

2023-10-10T11:19:54.081051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:19:54.307870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 7406
33.3%
per 7406
33.3%
million 7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103684
82.4%
Space Separator 14812
 
11.8%
Uppercase Letter 7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 14812
14.3%
i 14812
14.3%
l 14812
14.3%
a 7406
7.1%
t 7406
7.1%
s 7406
7.1%
p 7406
7.1%
e 7406
7.1%
m 7406
7.1%
o 7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111090
88.2%
Common 14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 14812
13.3%
i 14812
13.3%
l 14812
13.3%
P 7406
6.7%
a 7406
6.7%
t 7406
6.7%
s 7406
6.7%
p 7406
6.7%
e 7406
6.7%
m 7406
6.7%
Other values (2) 14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 14812
11.8%
14812
11.8%
i 14812
11.8%
l 14812
11.8%
P 7406
 
5.9%
a 7406
 
5.9%
t 7406
 
5.9%
s 7406
 
5.9%
p 7406
 
5.9%
e 7406
 
5.9%
Other values (3) 22218
17.6%

CO Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct736
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20885728
Minimum0.045833
Maximum0.554792
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:54.655393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.045833
5-th percentile0.1
Q10.1375
median0.1875
Q30.270833
95-th percentile0.382708
Maximum0.554792
Range0.508959
Interquartile range (IQR)0.133333

Descriptive statistics

Standard deviation0.089448203
Coefficient of variation (CV)0.42827429
Kurtosis-0.20976773
Mean0.20885728
Median Absolute Deviation (MAD)0.0625
Skewness0.73170672
Sum1546.797
Variance0.0080009811
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.005106551243
2023-10-10T11:19:55.054023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-10T11:19:56.037135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:56.338064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.1 480
 
6.5%
0.2 196
 
2.6%
0.133333 152
 
2.1%
0.129167 144
 
1.9%
0.175 142
 
1.9%
0.158333 138
 
1.9%
0.1375 138
 
1.9%
0.1625 136
 
1.8%
0.108333 132
 
1.8%
0.120833 130
 
1.8%
Other values (726) 5618
75.9%
ValueCountFrequency (%)
0.045833 4
 
0.1%
0.054167 8
0.1%
0.058333 8
0.1%
0.0625 8
0.1%
0.066667 8
0.1%
0.070833 18
0.2%
0.075 16
0.2%
0.079167 14
0.2%
0.083333 18
0.2%
0.086957 2
 
< 0.1%
ValueCountFrequency (%)
0.554792 2
< 0.1%
0.504417 2
< 0.1%
0.504167 2
< 0.1%
0.4875 2
< 0.1%
0.479167 2
< 0.1%
0.477833 2
< 0.1%
0.475 2
< 0.1%
0.473375 2
< 0.1%
0.470875 2
< 0.1%
0.470833 4
0.1%
2023-10-10T11:19:55.405588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ)

HIGH CORRELATION 

Distinct380
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35302579
Minimum0.1
Maximum1.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:56.756242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.3
Q30.4975
95-th percentile0.7
Maximum1.2
Range1.1
Interquartile range (IQR)0.2975

Descriptive statistics

Standard deviation0.17852329
Coefficient of variation (CV)0.50569475
Kurtosis0.63493544
Mean0.35302579
Median Absolute Deviation (MAD)0.1
Skewness0.91722254
Sum2614.509
Variance0.031870565
MonotonicityNot monotonic
2023-10-10T11:19:57.105421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1818
24.5%
0.3 1496
20.2%
0.4 1025
13.8%
0.5 702
 
9.5%
0.1 550
 
7.4%
0.6 392
 
5.3%
0.7 178
 
2.4%
0.8 116
 
1.6%
0.9 40
 
0.5%
0.332 10
 
0.1%
Other values (370) 1079
14.6%
ValueCountFrequency (%)
0.1 550
7.4%
0.139 2
 
< 0.1%
0.154 2
 
< 0.1%
0.16 2
 
< 0.1%
0.166 2
 
< 0.1%
0.167 2
 
< 0.1%
0.17 2
 
< 0.1%
0.171 4
 
0.1%
0.172 2
 
< 0.1%
0.173 2
 
< 0.1%
ValueCountFrequency (%)
1.2 2
 
< 0.1%
1.1 2
 
< 0.1%
1.072 2
 
< 0.1%
1.028 2
 
< 0.1%
1.017 2
 
< 0.1%
1 8
0.1%
0.986 2
 
< 0.1%
0.965 2
 
< 0.1%
0.959 2
 
< 0.1%
0.951 4
0.1%

CO 1st Max Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1130165
Minimum0
Maximum23
Zeros2491
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:57.404772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q39
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7759859
Coefficient of variation (CV)1.0932051
Kurtosis-0.47742695
Mean7.1130165
Median Absolute Deviation (MAD)6
Skewness0.92196079
Sum52679
Variance60.465956
MonotonicityNot monotonic
2023-10-10T11:19:57.681432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2491
33.6%
7 873
 
11.8%
8 738
 
10.0%
21 416
 
5.6%
1 382
 
5.2%
6 382
 
5.2%
22 382
 
5.2%
9 326
 
4.4%
23 294
 
4.0%
2 202
 
2.7%
Other values (14) 920
 
12.4%
ValueCountFrequency (%)
0 2491
33.6%
1 382
 
5.2%
2 202
 
2.7%
3 128
 
1.7%
4 70
 
0.9%
5 140
 
1.9%
6 382
 
5.2%
7 873
 
11.8%
8 738
 
10.0%
9 326
 
4.4%
ValueCountFrequency (%)
23 294
4.0%
22 382
5.2%
21 416
5.6%
20 182
2.5%
19 106
 
1.4%
18 54
 
0.7%
17 22
 
0.3%
16 4
 
0.1%
15 6
 
0.1%
14 6
 
0.1%

CO AQI
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct8
Distinct (%)0.2%
Missing3703
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean3.2749122
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size115.7 KiB
2023-10-10T11:19:58.060438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7719938
Coefficient of variation (CV)0.54108131
Kurtosis-0.58990902
Mean3.2749122
Median Absolute Deviation (MAD)1
Skewness0.69540793
Sum12127
Variance3.139962
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.01398145428
2023-10-10T11:19:58.430010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
2023-10-10T11:19:59.333102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps10
min4 days
max8 weeks and 1 day
mean2 weeks, 2 days and 12 hours
std2 weeks, 4 days and 6 hours
2023-10-10T11:19:59.803517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 1224
 
16.5%
3 880
 
11.9%
5 617
 
8.3%
1 428
 
5.8%
6 392
 
5.3%
7 132
 
1.8%
8 20
 
0.3%
9 10
 
0.1%
(Missing) 3703
50.0%
ValueCountFrequency (%)
1 428
 
5.8%
2 1224
16.5%
3 880
11.9%
5 617
8.3%
6 392
 
5.3%
7 132
 
1.8%
8 20
 
0.3%
9 10
 
0.1%
ValueCountFrequency (%)
9 10
 
0.1%
8 20
 
0.3%
7 132
 
1.8%
6 392
 
5.3%
5 617
8.3%
3 880
11.9%
2 1224
16.5%
1 428
 
5.8%
2023-10-10T11:19:58.713320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-10-10T11:19:17.208134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:24.467423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:28.024079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:31.293355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:34.782932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:38.382192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:41.863076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:45.676383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:49.034620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:52.462556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:56.211315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:59.716212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:03.066692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:06.788419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:10.164434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:13.631270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:17.434814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:24.689798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:28.254388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:31.490612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:34.999061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:38.634683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:42.086464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:45.882605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:49.247169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:52.680734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:56.433670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:59.933125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:03.286331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:06.994124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:10.381317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:13.844287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:17.641290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:24.889569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:28.435237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:31.674913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:35.198492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:38.826172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:42.289013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:46.070872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:49.452635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:52.886739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:56.628856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:00.121191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:03.486356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:07.182320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:10.600630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:14.040223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:17.854116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:25.087433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:28.624185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:31.863404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:35.422074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:39.028593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:42.496640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:46.269864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:49.658301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:53.087763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:56.854122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:00.311196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:03.694210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:07.373479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:10.817387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:14.243011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:18.098413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:25.343313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:28.843889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:32.308783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:35.658187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:39.264633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:42.750844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:46.494381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:49.886976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:53.322750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:57.090829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:00.530622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:03.937627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:07.599687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:11.049615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:14.462505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:18.331921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:25.562487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:29.039839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:32.514297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:35.875233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:39.488404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:42.965267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:46.708454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:50.092246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:53.545955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:57.314003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:00.733955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:04.158662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:07.816964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:11.260814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:14.673533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:18.576560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:25.804629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:29.266549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:32.740721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:36.115264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:39.724125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:43.406534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:46.927568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:50.320297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:53.795578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:57.537651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:00.962671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:04.394872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:08.049193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:11.485965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:14.904317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:18.786288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:26.018736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:29.449205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:32.929759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:36.328725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:39.919937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:43.624372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:47.119955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:50.522613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:53.999702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:57.735698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:01.163136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:04.592912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:08.256595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:11.696417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:15.103270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:19.003410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:26.246498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:29.644205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:33.141608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:36.546036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:40.126030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:43.842280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:47.314224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:50.737810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:54.432953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:57.938748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:01.359215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:04.807470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:08.462084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:11.902834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:15.301767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:19.255771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:26.466316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:29.862739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:33.362707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:36.771532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:40.364545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:44.083531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:47.533147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:50.956162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:54.649787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:58.163885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:01.579126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:05.047807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:08.678023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:12.125844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:15.533919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:19.501080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:26.694551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:30.078078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:33.567699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:36.988787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:40.594007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:44.307773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:47.756093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:51.185439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:54.883946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:58.388698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:01.794245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:05.477785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:08.886543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:12.339027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:15.751796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:19.716733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:26.891864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:30.267964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:33.757776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:37.200535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:40.787554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:44.519411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:47.955629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:51.384386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:55.087688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:58.606846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:02.009253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:05.678186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:09.085913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:12.539520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:15.941738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:19.964306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:27.140230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:30.483728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:33.969260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:37.459458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:41.015757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:44.775108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:48.192731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:51.608936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:55.319534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:58.842993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:02.229771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:05.902406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:09.319813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:12.759437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:16.374095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:20.192503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:27.359731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:30.683140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:34.160192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:37.668669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:41.215671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:44.987890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:48.398320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:51.816706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:55.537277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:59.047407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:02.423107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:06.132133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:09.517181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:12.960057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:16.574401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:20.432808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:27.581595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:30.884240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:34.370941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:37.887245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:41.427031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:45.219733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:48.602299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:52.018807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:55.751501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:59.265191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:02.631299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:06.348759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:09.733120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:13.168550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:16.780959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:20.668540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:27.783167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:31.068801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:34.563002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:38.097408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:41.632393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:45.431675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:48.801455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:52.215291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:55.962084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:18:59.487051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:02.827439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:06.555524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:09.932341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:13.381536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:19:16.973504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-10T11:20:00.111975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
NO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO MeanCO 1st Max ValueCO 1st Max HourCO AQI
NO2 Mean1.0000.8870.2740.887-0.755-0.4850.081-0.5130.3280.2040.3670.0070.7440.7400.2160.752
NO2 1st Max Value0.8871.0000.2970.999-0.608-0.3490.047-0.3840.3130.2010.326-0.0080.6490.7040.2630.685
NO2 1st Max Hour0.2740.2971.0000.295-0.259-0.117-0.008-0.119-0.020-0.0480.112-0.0720.1820.1720.2810.131
NO2 AQI0.8870.9990.2951.000-0.607-0.3480.047-0.3840.3130.2020.327-0.0080.6480.7030.2610.684
O3 Mean-0.755-0.608-0.259-0.6071.0000.8890.0470.879-0.123-0.035-0.2670.096-0.697-0.668-0.203-0.698
O3 1st Max Value-0.485-0.349-0.117-0.3480.8891.0000.1250.984-0.0170.027-0.1540.099-0.516-0.489-0.122-0.533
O3 1st Max Hour0.0810.047-0.0080.0470.0470.1251.0000.1250.0650.0770.0220.0640.0540.038-0.0380.024
O3 AQI-0.513-0.384-0.119-0.3840.8790.9840.1251.000-0.050-0.003-0.1660.083-0.468-0.470-0.115-0.508
SO2 Mean0.3280.313-0.0200.313-0.123-0.0170.065-0.0501.0000.8930.2110.6860.1700.1810.0090.179
SO2 1st Max Value0.2040.201-0.0480.202-0.0350.0270.077-0.0030.8931.0000.2110.8080.0660.091-0.0120.086
SO2 1st Max Hour0.3670.3260.1120.327-0.267-0.1540.022-0.1660.2110.2111.0000.0350.2620.2710.1010.286
SO2 AQI0.007-0.008-0.072-0.0080.0960.0990.0640.0830.6860.8080.0351.000-0.078-0.069-0.048-0.072
CO Mean0.7440.6490.1820.648-0.697-0.5160.054-0.4680.1700.0660.262-0.0781.0000.8580.1890.920
CO 1st Max Value0.7400.7040.1720.703-0.668-0.4890.038-0.4700.1810.0910.271-0.0690.8581.0000.3901.000
CO 1st Max Hour0.2160.2630.2810.261-0.203-0.122-0.038-0.1150.009-0.0120.101-0.0480.1890.3901.0000.257
CO AQI0.7520.6850.1310.684-0.698-0.5330.024-0.5080.1790.0860.286-0.0720.9201.0000.2571.000

Missing values

2023-10-10T11:19:21.091008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T11:19:21.934655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-10T11:19:22.377387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2010-10-014191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1708330.390.0Parts per million0.1583330.38NaN
2010-10-014191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1708330.390.0Parts per million0.1791670.303.0
2010-10-014191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1375000.211NaNParts per million0.1583330.38NaN
2010-10-014191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1375000.211NaNParts per million0.1791670.303.0
2010-10-024191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1500000.4120.0Parts per million0.1208330.21NaN
2010-10-024191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1500000.4120.0Parts per million0.1375000.212.0
2010-10-024191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1000000.314NaNParts per million0.1208330.21NaN
2010-10-024191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1000000.314NaNParts per million0.1375000.212.0
2010-10-034191028400 W RIVER ROADArizonaPimaTucson2010-10-03Parts per billion5.57083311.5010Parts per million0.0312080.0451138Parts per billion0.0500000.100.0Parts per million0.1083330.26NaN
2010-10-034191028400 W RIVER ROADArizonaPimaTucson2010-10-03Parts per billion5.57083311.5010Parts per million0.0312080.0451138Parts per billion0.0500000.100.0Parts per million0.1000000.101.0
State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2016-03-294191028400 W RIVER ROADArizonaPimaTucson2016-03-29Parts per billion1.78755.865Parts per million0.0414170.0451042Parts per billion0.0000000.000.0Parts per million0.1888330.2156NaN
2016-03-294191028400 W RIVER ROADArizonaPimaTucson2016-03-29Parts per billion1.78755.865Parts per million0.0414170.0451042Parts per billion0.0000000.000.0Parts per million0.2000000.20002.0
2016-03-304191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0000000.02NaNParts per million0.2048330.26622NaN
2016-03-304191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0000000.02NaNParts per million0.2000000.20002.0
2016-03-304191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0083330.1210.0Parts per million0.2048330.26622NaN
2016-03-304191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0083330.1210.0Parts per million0.2000000.20002.0
2016-03-314191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0541670.270.0Parts per million0.2541670.30053.0
2016-03-314191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0250000.18NaNParts per million0.2774580.49222NaN
2016-03-314191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0541670.270.0Parts per million0.2774580.49222NaN
2016-03-314191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0250000.18NaNParts per million0.2541670.30053.0